In the evolving landscape of artificial intelligence (AI) and machine learning (ML) technologies, the prevalence of biases poses significant challenges to the concept of fairness. Biases in AI and ML algorithms can perpetuate discrimination and inequity, impacting various aspects of society, from healthcare to finance and beyond. Understanding the different types of biases, their implications on fairness, and the ethical considerations involved in addressing them is crucial for shaping a more equitable and just future for AI and ML technologies.